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ICCV
2005
IEEE
15 years 11 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
BMCBI
2010
118views more  BMCBI 2010»
14 years 9 months ago
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
90
Voted
TIP
2010
97views more  TIP 2010»
14 years 4 months ago
Image Clustering Using Local Discriminant Models and Global Integration
In this paper, we propose a new image clustering algorithm, referred to as Clustering using Local Discriminant Models and Global Integration (LDMGI). To deal with the data points s...
Yi Yang, Dong Xu, Feiping Nie, Shuicheng Yan, Yuet...
58
Voted
MVA
2007
159views Computer Vision» more  MVA 2007»
14 years 11 months ago
Action Recognition of Insects Using Spectral Clustering
We propose a technique to recognize actions of grasshoppers based on spectral clustering. We track the object in 3D and construct features using 3D object movement in segments of ...
Maryam Moslemi Naeini, Greg Dutton, Kristina Rothl...
BMCBI
2004
111views more  BMCBI 2004»
14 years 9 months ago
Multiclass discovery in array data
Background: A routine goal in the analysis of microarray data is to identify genes with expression levels that correlate with known classes of experiments. In a growing number of ...
Yingchun Liu, Markus Ringnér